---

license: cc-by-nc-4.0  
language:
- hu
- en  
metrics:
- accuracy
- f1  
model-index:
- name: Hun_Eng_RoBERTa_base_Plain  
  results:
  - task:
      type: text-classification  
    metrics:
      - type: accuracy  
        value: 0.75 (hu) / 0.65 (en)  
      - type: f1  
        value: 0.74 (hu) / 0.64 (en)  
widget:
- text: "A tanúsítvány meghatározott adatainak a 2008/118/EK irányelv IV. fejezete szerinti szállításához szükséges adminisztratív okmányban..."
  example_title: "Incomprehensible"
- text: "Az AEO-engedély birtokosainak listáján – keresésre – megjelenő információk: az engedélyes neve, az engedélyt kibocsátó ország..."
  example_title: "Comprehensible"

---

## Model description

Cased fine-tuned `XLM-RoBERTa-base` model for Hungarian and English, trained on datasets provided by the National Tax and Customs Administration - Hungary (NAV) and translated versions of the same dataset using Google Translate API.

## Intended uses & limitations

The model is designed to classify sentences as either "comprehensible" or "not comprehensible" (according to Plain Language guidelines):
* **Label_0** - "comprehensible" - The sentence is in Plain Language.
* **Label_1** - "not comprehensible" - The sentence is **not** in Plain Language.

## Training

Fine-tuned version of the original `xlm-roberta-base` model, trained on a dataset of Hungarian legal and administrative texts. The model was also trained on the translated version of this dataset (via Google Translate API) for English classification.

## Eval results

### Hungarian Results:
| Class | Precision | Recall | F1-Score |
| ----- | --------- | ------ | -------- |
| **Comprehensible / Label_0** | **0.82** | **0.62** | **0.70** |
| **Not comprehensible / Label_1** | **0.71** | **0.88** | **0.78** |
| **accuracy** | | | **0.75** |
| **macro avg** | **0.77** | **0.75** | **0.74** |
| **weighted avg** | **0.76** | **0.75** | **0.74** |

### English Results:
| Class | Precision | Recall | F1-Score |
| ----- | --------- | ------ | -------- |
| **Comprehensible / Label_0** | **0.70** | **0.50** | **0.58** |
| **Not comprehensible / Label_1** | **0.63** | **0.80** | **0.70** |
| **accuracy** | | | **0.65** |
| **macro avg** | **0.66** | **0.65** | **0.64** |
| **weighted avg** | **0.66** | **0.65** | **0.64** |

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained("uvegesistvan/Hun_Eng_RoBERTa_base_Plain")
model = AutoModelForSequenceClassification.from_pretrained("uvegesistvan/Hun_Eng_RoBERTa_base_Plain")
```

### BibTeX entry and citation info

If you use the model, please cite the following dissertation (to be submitted for workshop discussion):

Bibtex:
```bibtex
@PhDThesis{ Uveges:2024,
  author = {{"U}veges, Istv{\'a}n},
  title  = {K{\"o}z{\'e}rthet{\"o} és automatiz{\'a}ci{\'o} - k{\'i}s{\'e}rletek a jog, term{\'e}szetesnyelv-feldolgoz{\'a}s {\'e}s informatika hat{\'a}r{\'a}n.},
  year   = {2024},
  school = {Szegedi Tudom{\'a}nyegyetem}
}
```